Manufacturing and Utilities Libraries - Page 5

Set of KNX Nodes with optional ETS group address importer.

JavaScript 108Updated: 2 y ago License: Permissive (MIT)

Design multi-agent environments and simple reward functions such that social driving behavior emerges

Python 107Updated: 4 y ago License: Permissive (MIT)

Example Azure Pipeline to train and deploy a machine learning model using the Azure Machine Learning service

Python 106Updated: 2 y ago License: Permissive (CC-BY-4.0)

Unified Image and Video Saliency Modeling (ECCV 2020)

Python 105Updated: 2 y ago License: Permissive (Apache-2.0)

A License-Plate detecttion application based on YOLO

Python 104Updated: 2 y ago License: Strong Copyleft (GPL-3.0)

Predicts Daily NBA Games Using a Logistic Regression Model

Python 104Updated: 2 y ago License: No License (No License)

Infinite Axis Utility System for Unity

C# 104Updated: 2 y ago License: No License (No License)

Javascript 3D satellite tracker library with up-to-date data from CELESTRAK. Uses Three.js, React and satellite.js for orbit prediction.

JavaScript 103Updated: 2 y ago License: Permissive (MIT)

A package for animating ragdolls through keyframed animations.

C# 103Updated: 2 y ago License: Permissive (MIT)

G-SchNet - a generative model for 3d molecular structures

Python 102Updated: 2 y ago License: Permissive (MIT)

Python module to read status and energy monitoring data from Tuya based WiFi smart devices. This includes state (on/off), current (mA), voltage (V), and power (wattage).

Python 101Updated: 2 y ago License: Permissive (MIT)

ASP.NET Core with Azure Service Bus

C# 101Updated: 2 y ago License: No License (No License)

💾 Get moving with Super Grate; a free & open source Windows Profile Migration & Backup Utility. Super Grate is a GUI (Graphical User Interface) that assists Microsoft's USMT (User State Migration Utility) in performing remote migrations over a network connection.

C# 100Updated: 2 y ago License: Strong Copyleft (GPL-3.0)

A Python and R autograding solution

Python 100Updated: 2 y ago License: Permissive (BSD-3-Clause)

A utility that connects Microsoft Flight Simulator 2020 with ForeFlight, Sky Demon, Garmin Pilot, FlyQ EFB, and probably a few more EFB apps

C# 98Updated: 2 y ago License: Permissive (Unlicense)

.Net Building Blocks

C# 97Updated: 2 y ago License: Permissive (MIT)

Repo that relates to the Medium blog 'Keeping your ML model in shape with Kafka, Airflow' and MLFlow'

Python 96Updated: 3 y ago License: No License (No License)

🚀 Streamline club management with our all-in-one system: bulk mailer, certificate generator & more. Perfect for student organizations.

PHP 96Updated: 2 y ago License: Permissive (MIT)

Traditional Machine Learning Models for Large-Scale Datasets in PyTorch.

Python 93Updated: 2 y ago License: Permissive (MIT)

Kafka-ML: connecting the data stream with ML/AI frameworks (now TensorFlow and PyTorch!)

Python 93Updated: 2 y ago License: Permissive (MIT)

Python 92Updated: 2 y ago License: Permissive (Apache-2.0)

The Open Source Computer Aided Dispatch (CAD), Personnel, Shift Management, Automatic Vehicle Location (AVL) and Emergency Management Platform that powers Resgrid.com

C# 92Updated: 2 y ago License: Permissive (Apache-2.0)

Implement Motion Planning for autonomous car on CARLA simulator

Python 91Updated: 2 y ago License: No License (No License)

Deep reinforcement learning based energy management strategy for hybrid electric vehicle

Jupyter Notebook 91Updated: 2 y ago License: No License (No License)

A tool for tracking blogs in orbit around Seneca's open source involvement

JavaScript 91Updated: 2 y ago License: Permissive (BSD-2-Clause)

midi processor library for PerformanceRNN & MusicTransformer published by "Google Magenta"

Python 91Updated: 2 y ago License: No License (No License)

A Google Earth Engine based algorithm that extracts river centerlines and widths from satellite images

Python 90Updated: 2 y ago License: Proprietary (Proprietary)

An open source platform for the Industrial Internet of Things, it acts as an edge gateway between sensor devices and cloud storage systems.

Python 90Updated: 2 y ago License: Permissive (Apache-2.0)

An autonomous vehicle written in python

Python 89Updated: 2 y ago License: Permissive (MIT)

MLOS is a Data Science powered infrastructure and methodology to democratize and automate Performance Engineering. MLOS enables continuous, instance-based, robust, and trackable systems optimization.

Python 89Updated: 2 y ago License: Permissive (MIT)

Disentagnled Graph Collaborative Filtering, SIGIR2020

Python 88Updated: 4 y ago License: No License (No License)

Implementation of Hang et al. 2020 "Hyperspectral Image Classification with Attention Aided CNNs" for tree species prediction

Python 88Updated: 2 y ago License: Permissive (MIT)

A pure Java HDF5 library

Java 88Updated: 2 y ago License: Permissive (MIT)

Custom error formatting and exception handling in Rest Controllers with Spring Boot

Java 86Updated: 2 y ago License: No License (No License)

:scroll: Visual Studio extension to generate OpenAPI (Swagger) web service reference.

C# 86Updated: 2 y ago License: Permissive (Apache-2.0)

Full stack, fully-featured social media web application using React, Firebase, Redux, Express, and Material-UI. Implemented backend REST API server with Node.js and Express, user login and authentication, image uploads, notifications, cloud functions, deploying to Firebase

JavaScript 85Updated: 2 y ago License: No License (No License)

General Modeling Network Specification

Jupyter Notebook 84Updated: 2 y ago License: No License (No License)

Package normalization ruleset for Repology

Python 84Updated: 2 y ago License: Strong Copyleft (GPL-3.0)

Python 82Updated: 2 y ago License: No License (No License)

# AD-Prediction Convolutional Neural Networks for Alzheimer's Disease Prediction Using Brain MRI Image ## Abstract Alzheimers disease (AD) is characterized by severe memory loss and cognitive impairment. It associates with significant brain structure changes, which can be measured by magnetic resonance imaging (MRI) scan. The observable preclinical structure changes provides an opportunity for AD early detection using image classification tools, like convolutional neural network (CNN). However, currently most AD related studies were limited by sample size. Finding an efficient way to train image classifier on limited data is critical. In our project, we explored different transfer-learning methods based on CNN for AD prediction brain structure MRI image. We find that both pretrained 2D AlexNet with 2D-representation method and simple neural network with pretrained 3D autoencoder improved the prediction performance comparing to a deep CNN trained from scratch. The pretrained 2D AlexNet performed even better (**86%**) than the 3D CNN with autoencoder (**77%**). ## Method #### 1. Data In this project, we used public brain MRI data from **Alzheimers Disease Neuroimaging Initiative (ADNI)** Study. ADNI is an ongoing, multicenter cohort study, started from 2004. It focuses on understanding the diagnostic and predictive value of Alzheimers disease specific biomarkers. The ADNI study has three phases: ADNI1, ADNI-GO, and ADNI2. Both ADNI1 and ADNI2 recruited new AD patients and normal control as research participants. Our data included a total of 686 structure MRI scans from both ADNI1 and ADNI2 phases, with 310 AD cases and 376 normal controls. We randomly derived the total sample into training dataset (n = 519), validation dataset (n = 100), and testing dataset (n = 67). #### 2. Image preprocessing Image preprocessing were conducted using Statistical Parametric Mapping (SPM) software, version 12. The original MRI scans were first skull-stripped and segmented using segmentation algorithm based on 6-tissue probability mapping and then normalized to the International Consortium for Brain Mapping template of European brains using affine registration. Other configuration includes: bias, noise, and global intensity normalization. The standard preprocessing process output 3D image files with an uniform size of 121x145x121. Skull-stripping and normalization ensured the comparability between images by transforming the original brain image into a standard image space, so that same brain substructures can be aligned at same image coordinates for different participants. Diluted or enhanced intensity was used to compensate the structure changes. the In our project, we used both whole brain (including both grey matter and white matter) and grey matter only. #### 3. AlexNet and Transfer Learning Convolutional Neural Networks (CNN) are very similar to ordinary Neural Networks. A CNN consists of an input and an output layer, as well as multiple hidden layers. The hidden layers are either convolutional, pooling or fully connected. ConvNet architectures make the explicit assumption that the inputs are images, which allows us to encode certain properties into the architecture. These then make the forward function more efficient to implement and vastly reduce the amount of parameters in the network. #### 3.1. AlexNet The net contains eight layers with weights; the first five are convolutional and the remaining three are fully connected. The overall architecture is shown in Figure 1. The output of the last fully-connected layer is fed to a 1000-way softmax which produces a distribution over the 1000 class labels. AlexNet maximizes the multinomial logistic regression objective, which is equivalent to maximizing the average across training cases of the log-probability of the correct label under the prediction distribution. The kernels of the second, fourth, and fifth convolutional layers are connected only to those kernel maps in the previous layer which reside on the same GPU (as shown in Figure1). The kernels of the third convolutional layer are connected to all kernel maps in the second layer. The neurons in the fully connected layers are connected to all neurons in the previous layer. Response-normalization layers follow the first and second convolutional layers. Max-pooling layers follow both response-normalization layers as well as the fifth convolutional layer. The ReLU non-linearity is applied to the output of every convolutional and fully-connected layer.  The first convolutional layer filters the 224x224x3 input image with 96 kernels of size 11x11x3 with a stride of 4 pixels (this is the distance between the receptive field centers of neighboring neurons in a kernel map). The second convolutional layer takes as input the (response-normalized and pooled) output of the first convolutional layer and filters it with 256 kernels of size 5x5x48. The third, fourth, and fifth convolutional layers are connected to one another without any intervening pooling or normalization layers. The third convolutional layer has 384 kernels of size 3x3x256 connected to the (normalized, pooled) outputs of the second convolutional layer. The fourth convolutional layer has 384 kernels of size 3x3x192 , and the fifth convolutional layer has 256 kernels of size 3x3x192. The fully-connected layers have 4096 neurons each. #### 3.2. Transfer Learning Training an entire Convolutional Network from scratch (with random initialization) is impractical[14] because it is relatively rare to have a dataset of sufficient size. An alternative is to pretrain a Conv-Net on a very large dataset (e.g. ImageNet), and then use the ConvNet either as an initialization or a fixed feature extractor for the task of interest. Typically, there are three major transfer learning scenarios: **ConvNet as fixed feature extractor:** We can take a ConvNet pretrained on ImageNet, and remove the last fully-connected layer, then treat the rest structure as a fixed feature extractor for the target dataset. In AlexNet, this would be a 4096-D vector. Usually, we call these features as CNN codes. Once we get these features, we can train a linear classifier (e.g. linear SVM or Softmax classifier) for our target dataset. **Fine-tuning the ConvNet:** Another idea is not only replace the last fully-connected layer in the classifier, but to also fine-tune the parameters of the pretrained network. Due to overfitting concerns, we can only fine-tune some higher-level part of the network. This suggestion is motivated by the observation that earlier features in a ConvNet contains more generic features (e.g. edge detectors or color blob detectors) that can be useful for many kind of tasks. But the later layer of the network becomes progressively more specific to the details of the classes contained in the original dataset. **Pretrained models:** The released pretrained model is usually the final ConvNet checkpoint. So it is common to see people use the network for fine-tuning. #### 4. 3D Autoencoder and Convolutional Neural Network We take a two-stage approach where we first train a 3D sparse autoencoder to learn filters for convolution operations, and then build a convolutional neural network whose first layer uses the filters learned with the autoencoder.  #### 4.1. Sparse Autoencoder An autoencoder is a 3-layer neural network that is used to extract features from an input such as an image. Sparse representations can provide a simple interpretation of the input data in terms of a small number of \parts by extracting the structure hidden in the data. The autoencoder has an input layer, a hidden layer and an output layer, and the input and output layers have same number of units, while the hidden layer contains more units for a sparse and overcomplete representation. The encoder function maps input x to representation h, and the decoder function maps the representation h to the output x. In our problem, we extract 3D patches from scans as the input to the network. The decoder function aims to reconstruct the input form the hidden representation h. #### 4.2. 3D Convolutional Neural Network Training the 3D convolutional neural network(CNN) is the second stage. The CNN we use in this project has one convolutional layer, one pooling layer, two linear layers, and finally a log softmax layer. After training the sparse autoencoder, we take the weights and biases of the encoder from trained model, and use them a 3D filter of a 3D convolutional layer of the 1-layer convolutional neural network. Figure 2 shows the architecture of the network. #### 5. Tools In this project, we used Nibabel for MRI image processing and PyTorch Neural Networks implementation.

Python 82Updated: 2 y ago License: No License (No License)

Load custom javascript in browser context

JavaScript 81Updated: 2 y ago License: Weak Copyleft (MPL-2.0)

MAterials Simulation Toolkit for Machine Learning (MAST-ML)

Python 80Updated: 2 y ago License: Permissive (MIT)

Server component of the Simple Stock Management stock & inventory web app. Designed for small businesses & non-profits.

Python 80Updated: 2 y ago License: Strong Copyleft (GPL-3.0)

providing inter-service communication on kubernetes via minikube using istio framework and spring boot resttemplate

Java 79Updated: 2 y ago License: No License (No License)

Deploy and scale machine learning models with FastAPI, Redis and Docker

Python 79Updated: 4 y ago License: No License (No License)

modify self-attention model for EEG signal as input and image embedding layer as output

Python 79Updated: 2 y ago License: No License (No License)

A datalogger for a solar inverter. Stores data in influxdb and displays it in grafana. Has load diverting capability, to use the inverter's excess power

Python 79Updated: 2 y ago License: Strong Copyleft (GPL-3.0)

Official version of 'Weakly Supervised 3D object detection from Lidar Point Cloud'(ECCV2020)

Python 78Updated: 4 y ago License: Permissive (MIT)

User's custom models boilerplate

Java 78Updated: 2 y ago License: Proprietary (Proprietary)

Creates CycloneDX Software Bill of Materials (SBOM) from Node.js projects

JavaScript 78Updated: 3 y ago License: Proprietary (Proprietary)